Data-driven bus timetabling with spatial-temporal travel time

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiang Li ◽  
Yang Ming ◽  
Hongguang Ma ◽  
Kaitao (Stella) Yu
Keyword(s):  
Travel Time ◽  
Big Data Analytics ◽  
Solution Space ◽  
Mean Value ◽  
Gps Data ◽  
Maximum Probability ◽  
Content Type ◽  
Robust Model ◽  
Space Compression ◽  
Log Normal

PurposeTravel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.Design/methodology/approachThe authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.FindingsCase studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.Originality/valueThis study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

Travel time prediction in transport and logistics

2019 ◽  
Vol 49 (3) ◽  
pp. 277-306 ◽  
Author(s):  
Xia Li ◽  
Ruibin Bai ◽  
Peer-Olaf Siebers ◽  
Christian Wagner
Keyword(s):  
Travel Time ◽  
Model Fitting ◽  
Real Life ◽  
Gps Data ◽  
Travel Time Prediction ◽  
Time Data ◽  
Content Type ◽  
Time Prediction ◽  
Travel Time Data ◽  
Real Life Data

Purpose Many transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of information storage, as well as the quality of the prediction. This paper aims to systematically investigate various meta-data (features) that require significantly less storage space but provide sufficient information for high-quality travel time predictions. Design/methodology/approach The paper systematically studied the combinatorial effects of features and different model fitting strategies with two popular decision tree ensemble methods for travel time prediction, namely, random forests and gradient boosting regression trees. First, the investigation was conducted using pseudo travel time data that were generated using a pseudo travel time sampling algorithm, which allows generating travel time data using different noise processes so that the prediction performance under different travel conditions and noise characteristics can be studied systematically. The results and findings were then further compared and evaluated through a real-life case. Findings The paper provides empirical insights and guidelines about how raw GPS data can be reduced into a small-sized feature vector for the purposes of vehicle travel time prediction. It suggests that, add travel time observations from the previous departure time intervals are beneficial to the prediction, particularly when there is no other types of real-time information (e.g. traffic flow, speed) are available. It was also found that modular model fitting does not improve the quality of the prediction in all experimental settings used in this paper. Research limitations/implications The findings are primarily based on empirical studies on limited real-life data instances, and the results may lack generalisabilities. Therefore, the researchers are encouraged to test them further in more real-life data instances. Practical implications The paper includes implications and guidelines for the development of efficient GPS data storage and high-quality travel time prediction under different types of travel conditions. Originality/value This paper systematically studies the combinatorial feature effects for tree-ensemble-based travel time prediction approaches.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Rigorous time-domain analysis of statistically oriented graphene sheet fluctuations

2017 ◽  
Vol 36 (5) ◽  
pp. 1351-1363 ◽  
Author(s):  
Athanasios N. Papadimopoulos ◽  
Stamatios A. Amanatiadis ◽  
Nikolaos V. Kantartzis ◽  
Theodoros T. Zygiridis ◽  
Theodoros D. Tsiboukis
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Finite Difference ◽  
Surface Waves ◽  
Time Domain ◽  
Finite Difference Time Domain ◽  
Mean Value ◽  
Content Type ◽  
The Mean ◽  
Difference Time

Purpose Important statistical variations are likely to appear in the propagation of surface plasmon polariton waves atop the surface of graphene sheets, degrading the expected performance of real-life THz applications. This paper aims to introduce an efficient numerical algorithm that is able to accurately and rapidly predict the influence of material-based uncertainties for diverse graphene configurations. Design/methodology/approach Initially, the surface conductivity of graphene is described at the far infrared spectrum and the uncertainties of its main parameters, namely, the chemical potential and the relaxation time, on the propagation properties of the surface waves are investigated, unveiling a considerable impact. Furthermore, the demanding two-dimensional material is numerically modeled as a surface boundary through a frequency-dependent finite-difference time-domain scheme, while a robust stochastic realization is accordingly developed. Findings The mean value and standard deviation of the propagating surface waves are extracted through a single-pass simulation in contrast to the laborious Monte Carlo technique, proving the accomplished high efficiency. Moreover, numerical results, including graphene’s surface current density and electric field distribution, indicate the notable precision, stability and convergence of the new graphene-based stochastic time-domain method in terms of the mean value and the order of magnitude of the standard deviation. Originality/value The combined uncertainties of the main parameters in graphene layers are modeled through a high-performance stochastic numerical algorithm, based on the finite-difference time-domain method. The significant accuracy of the numerical results, compared to the cumbersome Monte Carlo analysis, renders the featured technique a flexible computational tool that is able to enhance the design of graphene THz devices due to the uncertainty prediction.

Estimating Link Travel Time with Sparse GPS Data on Highway Corridors

2015 ◽  
Vol 2477 (1) ◽  
pp. 7-17 ◽  
Author(s):  
Qi Gong ◽  
Teresa M. Adams ◽  
Xiubin Bruce Wang
Keyword(s):  
Travel Time ◽  
Gps Data ◽  
Link Travel Time

scholarly journals UNIVERSAL STRUCTURE OF THE PERSONAL INCOME DISTRIBUTION

Fractals ◽  
2001 ◽  
Vol 09 (04) ◽  
pp. 463-470 ◽  
Author(s):  
WATARU SOUMA
Keyword(s):  
Income Distribution ◽  
Asset Price ◽  
Mean Value ◽  
High Income ◽  
Personal Income ◽  
Middle Income ◽  
The Mean ◽  
Universal Structure ◽  
Log Normal ◽  
Log Normal Distribution

We investigate the Japanese personal income distribution in the high income range over the 112 years (1887–1998), and that in the middle income range over the 44 years (1955–1998). It is observed that the distribution pattern of the log-normal with power law tail is the universal structure. However, the indexes specifying the distribution differ from year to year. One of the index characterizing the distribution is the mean value of the log-normal distribution; the mean income in the middle income range. It is found that this value correlates linearly with the gross domestic product (GDP). To clarify the temporal change of the equality or inequality of the distribution, we analyze Pareto and Gibrat indexes, which characterize the distribution in the high income range and that in the middle income range, respectively. It is found for some years that there is no correlation between the high income and the middle income. It is also shown that the mean value of Pareto index equals to 2, and the change of this index is effected by the change of the asset price. From these analysis, we derive four constraints that must be satisfied by mathematical models.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Towards a general model explaining physical and digital counterfeits

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Francisco-Jose Molina-Castillo ◽  
Elfriede Penz ◽  
Barbara Stöttinger
Keyword(s):  
General Model ◽  
Purchase Intention ◽  
Global Scale ◽  
Future Research ◽  
Digital Piracy ◽  
Digital Products ◽  
Content Type ◽  
Self Identity ◽  
Robust Model ◽  
Research Goal

PurposeDemand for fake physical and digital products is a global phenomenon with substantive detrimental effects on companies and consumers. This raises various questions and issues, such as whether there are generalizable explanations of purchase intentions.Design/methodology/approachThis research is based on consumer samples from three different countries. This paper develops and tests a model based on the theory of planned behavior (TPB) to explain both the demand for counterfeits and digital piracy. Respondents were questioned about physical products (e.g. clothing, accessories) from well-known brands and digital products (e.g. software, music).FindingsSocially oriented motives such as embarrassment potential, ethical concerns and social norms explain the intention to purchase fake physical and digital products, while personally oriented motives (e.g. self-identity) have indirect effects but not a direct impact on purchase intention.Research limitations/implicationsAs our results show, we find evidence for a general model – contributing and supporting our first and primary research goal of providing a theoretically robust model that bridges the gap between two streams of literature.Practical implicationsThe fact that drivers of buying counterfeit physical and digital goods are similar across countries provides justification for companies and international organizations to bundle their efforts and thus leverage them more strongly on a global scale.Originality/valueWe provide a basis for consolidating future research on demand for counterfeits and pirated goods because underlying factors driving demand are similar across the three countries studied herein.

The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamad Bahrami ◽  
Sajjad Shokouhyar
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Culture

PurposeBig data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.Design/methodology/approachThe study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.FindingsThe results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.Originality/valueThe present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

Asymmetric effect of feature level sentiment on product rating: an application of bigram natural language processing (NLP) analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yun Kyung Oh ◽  
Jisu Yi
Keyword(s):  
Natural Language Processing ◽  
Customer Satisfaction ◽  
Natural Language ◽  
Language Processing ◽  
Big Data Analytics ◽  
Service Improvement ◽  
Asymmetric Effect ◽  
Content Type ◽  
Quality Dimensions ◽  
Product Ratings

PurposeThe evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings.Design/methodology/approachThis study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings.FindingsThe results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors.Originality/valueThis study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.

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